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Akana (Turkish NLP Toolkit)

Akana (named after Ak Ana, the primordial creator goddess in Turkic mythology) is a modern, blazingly fast Turkish Natural Language Processing toolkit written in Rust with seamless Python bindings via PyO3 and native hardware SIMD acceleration via StringZilla.

Repository: https://github.com/altaidevorg/akana

Key Features

Phonology & Orthography Engine:
- Turkish alphabet characteristics and locale-aware casing (ı $\leftrightarrow$ I, i $\leftrightarrow$ İ).
- Major (2-way A/E) and Minor (4-way I/İ/U/Ü) Vowel Harmony validation.
- Consonant softening / mutation ($p \rightarrow b$, $ç \rightarrow c$, $t \rightarrow d$, $k \rightarrow \check{g}/g$).
- Epenthetic vowel drop (burun $\rightarrow$ burnu, akıl $\rightarrow$ aklı).
- Consonant doubling / gemination (hak $\rightarrow$ hakkı, his $\rightarrow$ hissi).
Tokenization & Sentence Segmentation:
- Zero-allocation, rule-based Turkish tokenizer handling proper nouns with apostrophes (İstanbul'da), abbreviations (Prof., Dr., vb.), currencies, URLs, emails, hashtags, dates, and times.
- Sentence Boundary Detector with Turkish quotation and abbreviation lookahead.
Normalization & Spell Checking:
- Asciifier & De-asciifier for Turkish diacritics restoration.
- SIMD Spell Checker: Accelerated with StringZilla hardware instructions for ultra-fast Levenshtein / edit distance candidate scoring.
- Informal Text Normalizer: Spoken Turkish colloquialisms reduction (yapcam $\rightarrow$ yapacağım, geliyom $\rightarrow$ geliyorum, noldu $\rightarrow$ ne oldu) and letter elongation deduping (çooook $\rightarrow$ çok).
Dual-Engine Morphology Suite:
1. Standard Morphology (akana.Morphology):
  - 66,491-Item Root Lexicon: Zemberek's default dictionaries (TDK master dictionary, non-TDK words, proper nouns, person names and abbreviations), generated by scripts/build_full_lexicon.py with Zemberek's own POS and attribute rules, pronunciations and compound data, plus akana's supplements (scripts/lexicon/akana-legal.dict: HMK, TBK, AİHM, tebliğname, ...; given names and surnames of akana's PII lists that Zemberek lacks, akana-names.dict; misspellings read as nonstandard with the TDK lemma, akana-spelling.dict).
  - A port of Zemberek's morphotactics: 287 states and 989 suffix transitions with morphophonemic templates and conditions, and stem alternations generated at load time (kitab-ı, redd-i, burn-u), so misspellings such as kitapda, gelti or kitapı get no analysis.
  - Numbers, Roman numerals and abbreviations take the suffixes of their spoken form (1923'te, %50'si, 15., XIX., İİK'nın, TBMM'de).
  - Analyses are ranked by a corpus frequency prior estimated on court decisions mixed with the sentences of the UD Turkish treebanks, whose part-of-speech tags also split the words read in more than one part of speech (kararının is karar+P3sg+Gen, hakkı the noun hak, Ali the name).
  - Compound Word Decomposer: compounds the lexicon marks or productive patterns on lexicon roots (denizaltı $\rightarrow$ deniz + altı, başbakanlar $\rightarrow$ baş + bakanlar); mahkeme is not mah + keme.
  - Morphological Generator on the same graph (generate("kitap", ["Noun", "A3sg", "P1sg", "Dat"]) $\rightarrow$ "kitabıma"); a generated word always analyses back to the requested tags.
  - Context-aware Disambiguator: the corpus prior plus bounded context rules (genitive–possessive agreement, postposition case, light verbs, determiners).
2. Syntactic Expressive Morphology (akana.SyntacticMorphology) (Google FSMNLP 2019 format):
  - Inflectional Groups (IG): the standard analyzer's readings, split where Zemberek's morphotactics derive (participles, converbs, verbal nouns, voice, -Abil, -lIk, -lI, -sIz, -ki, the copula), each group with its POS and Universal Dependencies features as UD Turkish writes them (Case, Number, Person, Number[psor], Person[psor], Tense, Aspect, Evident, Mood=GenPot, Polarity, Voice, VerbForm).
  - Zero-Derivation Elimination: the lexicon lists cross-categorized roots (güzel as adjective, adverb and noun), and an adjective inflected as a noun (güzeller) is a noun; only the copula (evde-yim, güzel-di) opens a group of its own.
  - Same lexicon, ranking and coverage as the standard analyzer.
Modern & Classic Turkish Readability Suite:
- Kalyoncu (2025) Formula Suite: Multi-regression equations (Formulas 1–4, $R^2$ up to 0.99) with embedded 4,600-word familiarity lexicon and exact grade-level mapping (3. Sınıf Öncesi to Lisansüstü).
- Classical Formulas: Ateşman (1997), Çetinkaya-Uzun (2010), and Bezirci-Yılmaz (2010).
Turkish AI Writing Style Auditor & Humanizer Engine:
- Detects LLM writing signatures: punctuation anomalies (excessive em-dashes, semicolons with conjunctions, colons), predicate tense repetition (-mektedir ratio), rhythm monotony ($CV = \sigma / \mu$), bureaucratic connectors, translationese calques, tricolon lists, and hypophora questions.
- Actionable prompt generation across 5 registers (hukuki-idari, akademik-kurumsal, analitik-gazetecilik, deneme-blog, edebi-yaratıcı).
- Trained AI-text detector in every audit report (report.ai_detection, next to the rule-based ai_score, which counts style findings): calibrated probability that the text is AI-written, from 500 gradient-boosted trees over 75 signals (content statistics, mined AI phrases such as "Elbette", "önemli bir rol oynar", token-transition scores over Akana's own embedding tokenizer), with a Turkish "why AI" report. On a held-out OpenAI evaluation set it flags about 95 of 100 AI texts long enough to judge (65+ words), at 1% false alarms on separate human texts. Not proof of authorship; poems get no decision, academic text a warning, shorter or highly repetitive texts are not judged.
- For texts the detector judges AI, humanize_prompt starts with the signals that made the text look AI.
Syntax & Universal Dependencies:
- Rule-based dependency parser with Universal Dependencies relations and CoNLL-U output. On the UD Turkish test splits it attaches 55.9 % of words to the right head (UAS) and 41.9 % with the right relation too (LAS), where attaching every word to the next word that is not punctuation gets 51.8 % UAS (scripts/parser_ud_eval.py): a margin of four points, and on Atis (42.8 against 51.3) none. Trained parsers do much better: use it for rough structure.
High-Level NLP Primitives:
- Turkish Syllabification & Hyphenation.
- Number to Words Converter (Cardinals, Ordinals, Currency).
- Named Entity Recognition (PER, LOC, ORG, DATE, MONEY, PERCENT).
- Keyword Extraction (Turkish RAKE) & Extractive Summarization (TextRank).
Turkish Text Chunking Suite for RAG & LLMs (Chonkie-Inspired):
- SemanticChunker: Segments long Turkish documents at natural topic shifts using embedded 2-bit TurboQuant vector similarity drops.
- Adaptive Threshold Modes: Direct similarity, percentile (default: 0.75), standard_deviation ($\mu - k\sigma$), interquartile ($Q_1 - k \cdot IQR$), and auto.
- Similarity Smoothing: Built-in 5-point Savitzky-Golay and Moving Average digital filters to suppress high-frequency local noise in sentence transitions.
- SDPMChunker (Semantic Double-Pass Merge): Two-pass hierarchical merge algorithm creating dense, coherent chunks with high information packing for vector databases.
- SentenceChunker: Rule-based sentence chunking respecting Turkish grammatical boundaries with configurable token counts and overlaps.
- High Performance: >50,000–1,000,000 words/sec on CPU with exact UTF-8 character and byte offset preservation.
Embedded Turkish Sentence Embeddings (TurboQuant 2-Bit Model2Vec):
- Ultra-lightweight (2.5 MB): Built-in 2-bit quantized static sentence embedding model distilled from BGE-M3 (altaidevorg/turkish-bge-m3-model2vec-turboquant-2bit).
- 256-dimensional vector embeddings with 92.19% STSb-TR benchmark accuracy.
- High Throughput: >20,000 sentences/sec on CPU with zero deep learning runtime or PyTorch dependencies.
- Built-in cosine similarity and batch embedding support.
Grammatical Error Detection & Correction (GED/GEC):
- Rule-based and precision first: 33 rules in 17 error categories: clitics (de/da, ki, mi), consonant assimilation (kitapda $\rightarrow$ kitapta), vowel drop (şehire $\rightarrow$ şehre), over-narrowing (başlıyan $\rightarrow$ başlayan), number and proper-noun apostrophes (1923'de $\rightarrow$ 1923'te, Ankara'lı $\rightarrow$ Ankaralı), split -ebil (ola bilir $\rightarrow$ olabilir), indefinite determiners (bir çok $\rightarrow$ birçok), reduplications (elele $\rightarrow$ el ele) and more. 26 rules are on by default: the ones that leave correct, edited Turkish alone. The other 7 are experimental and run only when asked for. It does not cover all 25 GECTurk error types (see the benchmark).
- Selectable rules: every rule has a stable id (de-da-after-verb, mi-merged, ...); choose them per checker or per call (rules=, enable=, disable=, experimental=True in Python; RuleSet in Rust; --rules/--enable/--disable/--experimental in the CLI). akana.grammar_rules() and akana grammar --list-rules list them.
- Diagnostics: Turkish and English explanations, character offsets, the rule id, and the rule's measured precision as confidence.
Turkish PII Masking & Bi-Directional Restoration (KVKK-Aligned):
- 50+ Turkish PII Categories: Comprehensive recognition of personal identity identifiers: TCKN (Turkish Republic ID with Modulo-10/11 algorithmic validation), VKN (Tax ID), IBAN (MOD-97 checksum; IBAN, TCKN and VKN shapes after their trigger words are masked even when the checksum fails), Credit Card (Luhn check), Turkish and international person names and surnames (curated lists plus 4,592 given names from Zemberek; names that are also words, such as Deniz or Metin, need a title, a role, a surname or a capital in mid-sentence), GSM and landline phones, Addresses, Passports, Driving Licenses, Vehicle Plates, VIN, IMEI, MAC, IP Addresses, Port numbers, Age/Age ranges, Blood types, Health conditions, Passwords, API tokens (OpenAI, Anthropic sk-ant, GitLab glpat-, GitHub PAT, HuggingFace hf_, Groq gsk_, npm npm_, Stripe, SendGrid, JWT, Bearer, PEM, Webhooks, Database URLs), and OTP codes.
- Positive Private Date & URL Detection: Accurately isolates private personal dates (birth dates, appointment schedules, billing due dates) without false positives on calendar holidays or campaign end dates. Recognizes private URLs containing sensitive authentication and session tokens.
- Bi-Directional Restoration with Morphological Vowel Harmony: Automatically tracks an in-memory or serializable mapping dictionary to restore masked entities losslessly, adapting case and possessive suffixes in downstream text according to Turkish 2-way and 4-way vowel harmony (e.g. {{AD_1}}'a $\rightarrow$ Ahmet Yılmaz'a, {{AD_1}}'e $\rightarrow$ Mehmet Demir'e). Supports conversational session continuity via mask_with_vault to persist identifiers across multi-turn dialogues.
- Multiple Masking Modes:
  - placeholder: Standard structured tokens ({{AD_1}}, {{TCKN_1}}, {{IBAN_1}}) designed for clean downstream processing and 100% reversible restoration.
  - surrogate: Statistically and morphologically valid Turkish synthetic replacements (e.g., valid surrogate TCKNs, realistic Turkish names, valid surrogate IBANs).
  - tag: Standard semantic entity tags ([AD], [TCKN], [IBAN]).
  - anonymize: Category label redaction replacing sensitive data with uppercase entity tags ([AD], [TCKN], [SIFRE], [IBAN]).
- Smart Non-PII Filtering: Preserves corporate/functional emails (info@, destek@, satis@) and public dates while filtering individual personal data. Optionally keeps the names of well-known public figures (preserve_public_figures=True: Atatürk, Nazım Hikmet, Fatih Sultan Mehmet), only when the whole name is such a phrase.
- Informal Addresses: Abbreviated, lower-case and all-caps addresses as people type them (sedefli mh bayır sk. bina 89 D: 22), with the district and province around them.
- Optional Model2Vec Context Scoring (use_embeddings=True): Akana's built-in 256-dim Turkish embeddings decide ambiguous names with no other evidence, and find health conditions missing from the dictionary before a clinical trigger (fibromiyalji tanısı, not futbol hastasıyım). scripts/experiment_pii_embeddings.py measures where this helps and where trigger words do better.
- High Performance: >4,500 docs/sec on short messages (about 0.2 ms per 90-character document) on one CPU core.
High-Performance Architecture:
- Pure Rust core with zero JVM dependency.
- Python package via pyo3 and maturin (compatible with uv).
- Command Line Interface (CLI) for shell workflows.

Performance Benchmarks (Akana vs Zeyrek / Zemberek & StringZilla SIMD)

Tested on real Turkish text corpora and 10,500 morphological queries (benchmarks/). The Zeyrek column is from the 0.8.0 run of benchmarks/benchmark_vs_zeyrek.py and was not re-measured (Zeyrek is not installed here); Akana's morphology rows are the median of three runs of the same script on 0.9.0, from Python, on a laptop CPU.

Benchmark Metric	Zeyrek (Python Zemberek Port)	Akana (Rust + StringZilla SIMD)	Performance / Throughput
Root Lexicon	66,821 entries (Zemberek's six dictionaries)	66,491 items	The same dictionaries, merged, cleaned and supplemented (court terms, abbreviations)
Startup / Lexicon Init	`2,733.8 ms` (~2.7s)	`114.8 ms`	~24x faster
Morphological Parse (10.5k words)	`55,349.7 ms` (55.3s)	`178.7 ms`	~310x faster (`58,760 words/sec`; 42k–63k over three runs)
Tokenization (Zero-Allocation)	~230 words/sec	`949,991 tokens/sec`	>4,000x faster (<21 ms for 19.5k tokens)
Informal Normalization	N/A	`36,222 words/sec`	High Throughput (Zero-Regex Suffix Matching)
AI Writing Style Audit	N/A	`54,700 words/sec`	StringZilla SIMD rules + trained AI-text detector (10.2k words in 187 ms with `benchmarks/benchmark_stringzilla_optimizations.py`; the rules alone run at ~120k words/sec in the same script on the same machine)
Named Entity Recognition (NER)	N/A	`1.1–1.3 MB/sec`	trained tagger (averaged perceptron, Viterbi) for persons, places and organisations, patterns for dates, amounts and streets: 1 MB of RagTurk news, words cached per thread (the benchmark script's repeated sentence: ~3.6 MB/s). WikiNER test F1: PER 0.76, LOC 0.71, ORG 0.54 (`scripts/ner_eval.py`)
Grammar Checking (GED/GEC)	~22–55 sent/s (Neural)	`~7,000 sent/s`	default rules; the 20,769 GECTurk test sentences in 2.7–3.0 s from Python (`tests/benchmark_real_data.py`, three runs on a laptop CPU). With the experimental agreement rules, which run the disambiguator: ~1,650 sent/s
Turkish Embeddings	79 sent/s (BGE-M3)	`20,013 sent/s`	253x faster (2.5 MB 2-bit TurboQuant)
Sentence Chunking	N/A	`1,120,000 words/sec`	Zero-Allocation Slicing (<3 ms / doc)
Semantic Chunking (TurboQuant)	~80 words/sec (Neural)	`62,500 words/sec`	~780x faster (~40 ms for 200 sents)
Turkish PII Masking & Restoration	N/A (Standard Regex / Spacy)	`3,570 docs/sec`	Ultra-Fast KVKK Pipeline (<0.28 ms / doc, 50+ entity types)
Hardware Acceleration	Pure Python loops	StringZilla AVX-512 / AVX2 / NEON	Native SIMD Instructions

Morphological Analysis on Held-Out Treebanks (`scripts/ud_eval.py`)

The analyzer and the disambiguator on UD Turkish data that trains nothing akana ships: the test splits of the six treebanks whose train and dev sentences feed the ranking prior, and all of IMST (CC BY-NC-SA, used for evaluation only). BOUN's test split is broken down by genre. Coverage: the word has a reading. Class: the disambiguated reading's part of speech is UD's (a participle or verbal noun counts as a verb or as the class it derives; names, punctuation and symbols left out). Twins: the same, on the words whose readings fall in two classes or more, one of them UD's. Lemma: the reading's lemma or root equals UD's lemma; UD writes a verb's stem and some derivations differently, so this is agreement, not accuracy.

set	words	coverage %	class %	twins	twins %	lemma %
Atis	4784	98.9	89.4	1265	89.3	86.4
BOUN biographical	1797	99.4	88.9	641	90.0	92.6
BOUN essays	2192	99.0	85.4	844	83.2	89.6
BOUN instructional	1636	98.9	86.8	666	86.6	92.9
BOUN news	2541	98.4	88.4	742	83.2	90.8
BOUN popular culture	1821	99.0	86.5	721	82.5	92.0
FrameNet	1198	99.7	93.2	518	90.3	92.5
IMST	45998	99.2	86.3	18092	85.7	92.6
Kenet	15135	99.5	88.9	6291	85.7	90.4
Penn	8471	96.4	89.8	2844	87.9	90.7
Tourism	7915	99.8	96.1	3757	95.7	95.2
all	93488	99.0	88.2	36381	87.0	91.9

On the court-decision and RagTurk samples of the morphology sweep (3.6 M and 163 k tokens, examples/morph_sweep.rs), 0.49 % and 7.6 % of tokens have no reading (0.36 % and 2.3 % of lower-case words: the rest are names, foreign words and codes), with 2.2 and 1.9 readings per token.

Turkish Text Chunking Performance Benchmark (`scripts/benchmark_chunking.py`)

Evaluated on realistic multi-domain Turkish documents (50 paragraphs, ~200 sentences, ~2,500 words):

Strategy	Algorithm	Latency per Doc	Throughput (Words/Sec)	Throughput (Docs/Sec)	Key Benefit
`SentenceChunker`	Rule-Based Sentence Packing	2.23 ms	1,121,076 words/s	448.4 docs/s	Ultra-low latency, token overlaps
`SemanticChunker`	2-Bit Embedding Cosine Valleys	39.85 ms	62,735 words/s	25.1 docs/s	Natural topic boundary detection
`SDPMChunker`	Semantic Double-Pass Merge	44.10 ms	56,689 words/s	22.7 docs/s	Optimal chunk density & cohesion

🚀 High Throughput on CPU: SemanticChunker processes >62,000 words per second on a single CPU core without PyTorch, CUDA, or heavy ONNX dependencies.
📦 Zero External Infrastructure: Built-in 2.5 MB TurboQuant embeddings execute entirely in-process with minimal memory footprint.

Grammar Checker Precision and Recall (GECTurk, court decisions, Wikipedia)

A grammar checker that rewrites correct text does more harm than good, so each rule is measured on correct, edited Turkish as well as on errors:

Clean text: 7,500 sentences from Turkish court decisions (CC0; 1,500 of them are committed as crates/akana-core/tests/data/grammar_clean_court.txt) and 4,452 Wikipedia sentences from the RagTurk articles (measured locally, not redistributed).
Errors: the GGLab/GECTurk test set (20,769 sentences, 25 synthetic error types) and its 300 human-written movie-review sentences. A finding counts as right when it overlaps a token GECTurk labels as an error.

A rule is on by default only if it raises a false alarm on at most 1 in 1,000 clean sentences and, where GECTurk has its error type, at least 80 % of its GECTurk findings are on a labelled error. GECTurk labels only the errors it injected. For a rule whose errors it does not inject, such as proper-noun apostrophes or peşpeşe, every finding in the measured text was read and judged. The other rules are experimental: off unless asked for. cargo test -p akana-core runs the default rules over the committed clean sentences (tests/grammar_precision_gate.rs) and fails if any rule fires on more than 1 in 1,000 of them, or if correct changes more than 3.

Correct text changed by correct() (default rules; before = every rule as it was before the rule ids):

Text	Sentences	Before	After
Court decisions, committed gate corpus	1,500	16.5 %	0.0 %
Court decisions, local	6,000	18.0 %	0.8 %, all real errors in the source (yada, her hangi, İstekde, 2003'de)
Wikipedia (RagTurk)	4,452	13.0 %	0.2 %, all but one real errors (Milano'lu, senaristdir, 6.600'dan)

GECTurk (token-level error detection, python tests/benchmark_real_data.py):

Split	Rules	Precision	Recall	F0.5	Error-free sentences flagged
Test set (20,769)	before	79.5 %	38.1 %	65.3 %	8.0 %
Test set (20,769)	default	97.9 %	37.7 %	74.2 %	0.8 %
Test set (20,769)	all (`--experimental`)	96.4 %	37.6 %	73.4 %	1.5 %
Movie reviews (300)	before	63.8 %	53.8 %	61.5 %	21.3 %
Movie reviews (300)	default	98.1 %	55.2 %	84.9 %	1.3 %

Recall by GECTurk error type (default rules): the checker catches most yada (69/69), te/ta (16/16), lexicalised ki (344/344), indefinite determiners (1,176/1,224), -ebil (1,946/2,376), capitalisation (362/372) and colloquial forms (114/123), and fewer of the split locatives (1,270/11,120), merged de/da (798/1,934), merged mi (503/871) and over-narrowing (1,032/1,928). Earlier versions of this README claimed all 25 GECTurk categories; the checker has 18 error categories of its own (17 with a rule), and they do not map onto GECTurk's one to one. Nine GECTurk types have no rule and next to no recall: de/da harmony (0/13), loan-cluster epenthesis (0/59), merged reduplication such as zamanzaman (0/63), wrong softening (1/61), merged compound verbs (0/62), -hane/-name (0/16), circumflex (0/135), abbreviations (0/47) and one unidentified type (0/2); split compound verbs get 10/146. The GECTurk paper's neural models are scored with its own correction metric, not comparable to these numbers.

Every rule (clean text: findings per 1,000 sentences, before → after; * = every finding after the change was read and is a real error in the source; GECTurk precision by its labels, before → after, with the number of findings):

Rule	Default	Court (7,500)	Wikipedia (4,452)	GECTurk precision	Confidence
`capitalization`	on	0 → 0	0 → 0	89 % (419) → 89 % (409)	0.90
`mi-merged`	on	13.1 → 0	7.4 → 0	88 % (504) → 100 % (196)	0.99
`mi-merged-person`	on	0 → 0	0 → 0	97 % (63) → 98 % (42)	0.95
`mi-harmony`	on	0 → 0	0.2 → 0	0 % (1) → 0 % (1); judged 1/1 right	0.67
`de-da-hardened`	on	0.1 → 0.1*	25.8 → 0	22 % (73) → 89 % (18)	0.88
`de-da-merged-fixed`	on	0.7 → 0.7*	0 → 0	99 % (664) → 100 % (537)	0.99
`de-da-after-verb`	on	96.8 → 0.3*	50.3 → 0.4 (one typo split wrongly)	36 % (619) → 99.7 % (369)	0.99
`locative-split`	on	1.9 → 0	0.9 → 0	99 % (913) → 100 % (635)	0.99
`ki-lexicalized-split`	on	0.1 → 0.1*	0 → 0	98 % (175) → 98 % (175)	0.98
`ki-merged-fixed`	on	0.5 → 0.5*	0 → 0	100 % (84) → 100 % (84)	0.99
`ki-after-verb`	on	17.1 → 0	7.2 → 0	36 % (311) → 100 % (78)	0.99
`ordinal-dot-apostrophe`	on	0 → 0	0 → 0	no findings	0.5
`number-suffix`	on	0.1 → 0.1*	0 → 0.2*	0 % (5) → 0 % (5); judged 8/8 right	0.90
`apostrophe-derivational`	on	4.0 → 0.1*	34.6 → 1.3*	21 % (604) → 4 % (53); judged 62/62 right	0.98
`apostrophe-plural-name`	experimental	(in the above) → 0	(in the above) → 1.6	10 % (21); judged 19/28 right	0.67
`reduplication`	on	0 → 0	0 → 0	0 % (8) → 0 % (8); judged 8/8 right	0.90
`ebil-split`	on	10.8 → 0	1.6 → 0	89 % (1,125) → 100 % (939)	0.99
`indefinite-compound`	on	3.1 → 2.9*	0.7 → 0	94 % (631) → 96 % (612)	0.96
`compound-verb-merged`	on	0 → 0	0 → 0	0 % (4) → 0 % (4); judged 4/4 right	0.83
`compound-verb-split`	on	0 → 0	0 → 0	100 % (5) → 100 % (5)	0.86
`consonant-assimilation`	on	8.5 → 0.7*	0.4 → 0.2*	86 % (143) → 96 % (49)	0.94
`consonant-softening`	on	0 → 0	0 → 0	90 % (20) → 100 % (15)	0.94
`vowel-drop`	on	31.6 → 0	4.9 → 0	35 % (456) → 97 % (78)	0.96
`yor-narrowing`	on	0 → 0	0 → 0	no findings	0.5
`over-narrowing`	on	1.3 → 0.9*	0.7 → 0	99 % (1,057) → 100 % (953)	0.99
`colloquial-form`	on	0.4 → 0.1*	0 → 0	91 % (150) → 91 % (147)	0.91
`tautology`	on	0.3 → 0	0 → 0	0 % (4) → no findings	0.5
`plural-harmony`	experimental	0.1 → 0.1	1.1 → 0	0 % (3) → 0 % (2); judged 1/2 right	0.5
`quantity-plural`	experimental	0.3 → 0.3	1.8 → 0.4	4 % (97) → 0 % (5)	0.18
`genitive-possessive`	experimental	0 → 0	0 → 0	6 % (70) → 0 % (2)	0.25
`ne-ne-polarity`	experimental	0.3 → 0	0.7 → 0.2	25 % (12) → 0 % (1)	0.2
`negation-concord`	experimental	1.7 → 1.2	0.7 → 0.2	5 % (66) → 3 % (36)	0.05
`inanimate-plural-verb`	experimental	1.2 → 1.5	1.1 → 1.3	5 % (62) → 3 % (65)	0.04

Before, all rules ran together; after, each rule is measured alone. confidence is (right + 1) / (findings + 2) over a rule's GECTurk findings (by the labels where GECTurk has the error type, judged otherwise), or 0.5 for a rule that never fired. To reproduce: scripts/build_grammar_clean_corpus.py --local DIR writes the measurement sets, and cargo run --release -p akana-core --example grammar_rule_report -- --clean court=... --clean ragturk=... --gec gecturk=... prints the per-rule numbers (--context RULE prints each finding in its sentence).

Turkish Sentence Embedding Benchmark (Model2Vec TurboQuant 2-Bit)

Evaluated on standard Turkish Semantic Textual Similarity Benchmark (STSb) test sets:

Model	Vocab	Size	Compression	STSb (gorkem)	STSb (emrecan)	Speed	Speedup
BGE-M3 (Teacher)	250,002	~2,200 MB	1x	96.35%	79.57%	79 s/s	1.0x
Our Model	39,655	2.50 MB	880x	92.19%	63.53%	20,013 s/s	253x

⚡ 880x Model Compression: Compressed from ~2,200 MB to 2.50 MB embedded directly into the binary with zero runtime dependencies.
🚀 253x Speedup: Delivers 20,013 sentences/sec on CPU with high retention of semantic quality against the teacher model.

Ultra-Low-Memory Turkish RAG Retrieval Benchmark (RagTurk & Turkuaz-RAG)

Evaluated on metunlp/ragturk (Turkish agglutinative morphology failure modes, arXiv:2602.03652; first 300 articles of formal_5k: 2,732 chunks, 600 questions) and eneSadi/turkuaz-rag (multi-context QA; first 500 samples: 1,000 news texts, 500 questions) with compiled USearch (unum-cloud/usearch) metrics. Recall@k counts a question as answered when any of its gold passages is in the top k.

Retrieval Engine	Bytes / Vector	RAM Footprint (1M Docs)	Turkuaz Recall@10	Turkuaz MRR@10	RagTurk Recall@10	RagTurk MRR@10	Query Latency (RagTurk)
Dense FP32 (256-dim, exact cosine)	1,024 B	1,024 MB	75.4%	0.383	93.2%	0.772	1,095 µs
Akana Dense 2-Bit (TurboQuant)	64 B	`64 MB` (16x smaller)	73.8%	0.375	91.2%	0.738	481 µs
Akana Dense 1-Bit (Binary Hamming)	32 B	`32 MB` (32x smaller)	67.0%	0.329	86.3%	0.662	250 µs
Akana Morphological BM25 (inverted index)	Inverted	~116 MB	`87.6%`	`0.473`	`98.0%`	`0.880`	2,471 µs
Akana Single-Buffer Hybrid (128B)	128 B	`128 MB` (8x smaller)	83.6%	0.444	94.8%	0.789	1,453 µs
Akana Dual-Index RRF (2-Bit + BM25)	64 B + Inverted	~180 MB	83.2%	0.440	97.8%	0.837	3,975 µs
Akana Dual-Index RRF (1-Bit + BM25)	32 B + Inverted	~148 MB	81.8%	0.418	97.7%	0.812	3,942 µs

Latency is per query and end to end (query embedding + search) in a single Python process on a laptop CPU; the BM25 inverted index is the pure-Python one in the benchmark script, so the BM25 and RRF rows are dominated by Python. The FP32 row is exact brute-force cosine over akana.embed vectors.

Reproducibility: python scripts/benchmark_rag_real_data.py --dataset all --readme prints this table (defaults: 300 RagTurk articles, 500 Turkuaz samples; Turkuaz is streamed once and then read from .cache/rag_benchmarks/turkuaz.jsonl, so later runs work offline). Every quality figure is deterministic: two runs give the same reciprocal rank for every question. See also the runnable workflows in examples/11_ultra_low_memory_retrieval_usearch.py.

Correction (0.8.0): earlier versions of this table (and of the benchmark script, example and README snippets) built USearch indexes as Index(ndim=nbytes, dtype="u8") and added numpy uint8 arrays. USearch reads uint8 arrays as packed bits and unpacks them into such an index, so it kept only the first nbytes bits of each vector as 0/1 bytes: 1/8 of each dense vector, and none of the hybrid's sparse half. Every USearch row of the old table measured that loss (for example 36.7% RagTurk Recall@10 for the hybrid, and 1-bit appearing better than 2-bit). Indexes must store the packed bytes verbatim: Index(ndim=8 * nbytes, dtype="b1", metric=...), which also passes nbytes to the compiled metric. Any index built the old way has to be rebuilt. After Index.load() or Index.view(), set index.metric to the compiled metric again: USearch restores the metric from the file header, which cannot hold a function pointer (a reloaded b1 index crashes on search without it; a reloaded u8 index silently used USearch's own cosine).

Key Empirical Findings

2-bit TurboQuant keeps almost all of FP32's quality: 0.738 vs 0.772 MRR@10 on RagTurk and 0.375 vs 0.383 on Turkuaz, at 1/16 of the memory. 1-bit halves the memory again and costs another 0.05–0.08 MRR@10.
Morphological BM25 is the strongest single retriever on both sets (0.880 MRR@10 on RagTurk, 0.473 on Turkuaz; RagTurk questions share most of their vocabulary with their passages). Fusing it with 2-bit dense through RRF matches its RagTurk Recall@10 (97.8% vs 98.0%) but not its MRR@10 (0.837), and trails it on Turkuaz.
The 128-byte single-buffer hybrid is one memory-mappable index with no inverted index. It adds +0.05 (RagTurk) and +0.07 (Turkuaz) MRR@10 over 2-bit dense alone but stays below BM25: its sparse half holds the 16 heaviest terms of a passage (a RagTurk chunk has a median of 39 terms), weighted by repetition and part of speech. Function words are no terms (ve, için, bu, bir, tarafından), and only the twenty lemmas most frequent in both court decisions and general text (almak, vermek, yer ...) carry an IDF weight, because a full static IDF cost the hybrid 0.01–0.04 MRR@10 by trading a passage's repeated topic words for words it mentions once.
Hybrid vectors depend on the morphology build. 0.9.0's analyzer and sparse encoder change every sparse_embed vector and the sparse half of every hybrid vector built with 0.8.0 (dense halves are unchanged): rebuild such indexes. From 0.9.0 on, store akana.encoder_info() with an index and check it with akana.check_encoder() when you open it (see Index compatibility below); a release that changes vectors within the same sparse spec reports how many and keeps a stale index within 0.005 MRR@10 of a rebuilt one.
Embedding cost: on one core of a laptop CPU, the 128-byte hybrid embeds 170–240 court-decision chunks of about 214 words per second, the sparse half alone 200–220 and the dense half alone 1,600–2,100 (scripts/benchmark_embedding_cost.py, three runs; the machine's load moves these by a third). embed_batch_hybrid splits a batch of 512 or more texts across the CPU cores.

Index compatibility

An index stores what built it, and checks it when it is opened:

import akana

info = akana.encoder_info()   # save this dict next to the index when you build it
# {'akana_version': '0.9.0', 'unicode_version': ..., 'sparse_spec': '1.0', 'hybrid_layout': 1,
#  'dense_model': ..., 'analysis_version': '1.0', 'analysis_fingerprint': ...,
#  'sparse_fingerprint': ..., 'dense_fingerprint': ..., 'fingerprint': ..., 'resources': {...}}

status = akana.check_encoder(stored_info, "hybrid")   # when you open it: the dict, or its JSON text

The second argument is what the index holds: "sparse" (sparse_embed vectors, or a BM25 index of their terms), "dense" (embed and the packed 2-bit and 1-bit vectors) or "hybrid" (the default). Each is judged by what its vectors depend on, so a new dense model asks dense and hybrid indexes to rebuild but leaves sparse ones alone.

"identical": this library was built from the same inputs as that kind of vector needs (the resources, the source of its encoders, akana's own Cargo.lock, the Unicode version) and produces exactly the vectors the index holds.
"compatible": the same sparse spec major (sparse vectors), the same dense model, tokenizer and weights (dense vectors), both and the hybrid layout (hybrid vectors). Some vectors differ, as code or a dependency changed: releases that change them say how many, and keep at most 5 % of documents' vectors changed and a stale index's MRR@10 within 0.005 of a rebuilt one, so no rebuild is needed.
"rebuild": term ids, term definitions, the dense model or the vector layout changed, or the stored record is not one encoder_info() wrote. Re-embed the documents.

An index built with 0.8.0 or earlier stores no encoder info and must be rebuilt; CHANGELOG.md lists what else changed since 0.8.0 and how to migrate.

Cargo.lock and rust-toolchain.toml pin what the vectors depend on (the Unicode tables of the Rust standard library included), and ranking inside the analyzer and disambiguator is done in integers. The published wheels, and the sdist built into a wheel, are built with that toolchain and lock file and tested against the golden vectors on Linux, macOS and Windows before release. The fingerprints also cover the Unicode version and a probe set's vectors, so a build with another toolchain reports another fingerprint when either differs; a build without akana's own lock file (from crates.io, or as another project's dependency, whose resolved versions it cannot see) always reports one of its own: check_encoder then says "compatible", not "identical".

The hybrid metric's alpha (set_hybrid_metric_alpha, get_usearch_metric_pointer_hybrid(alpha)) is one value for the whole process: setting it changes the metric of every open index that uses it. It must be a number in [0, 1] (outside it is clamped; NaN raises ValueError).

Comparison with RAGTurk (`arXiv:2602.03652`) & Heavy Neural Pipelines

Pipeline	Model Weights	Vector RAM (1M Docs)	Query Latency	Turkish Recall@10	Computational Requirement
BGE-M3 (Teacher)	2,200 MB	4,096 MB (4.1 GB)	~45 ms	~85–90%	Heavy GPU / PyTorch
RAGTurk Baseline (EmbeddingGemma)	~600 MB	1,024 MB (1.0 GB)	~15 ms	~70–75%	Neural embedder, drops on morphology
RAGTurk "Production-Friendly" (MiniLM Reranker)	~730 MB	1,024 MB (1.0 GB)	40–80 ms	~90.1%	Requires cross-encoder over all candidate pairs
RAGTurk "Max Accuracy" (HyDE + LLM)	Multi-GB	1,024 MB (1.0 GB)	1,500–4,000 ms	~92.0%	Heavy LLM query generation ($3.7\times$ tokens)
Akana Dual-Index RRF (2-Bit + Morph BM25)	`2.50 MB`	`~180 MB`	`2.9 ms`	`97.7%` (RagTurk, 600 questions)	Zero LLMs, zero GPUs. Runs entirely on CPU

The neural rows are as reported for their own setups; the Akana row is measured on the 300-article RagTurk subset above, so the two are not a like-for-like comparison.

In Section 6.1 of the RAGTurk paper, the authors explicitly highlight the need to: "(iv) study Turkish morphology-aware retrieval features (e.g., lemma-aware sparse retrieval, morphological analyzers)." Akana directly solves this challenge natively in Rust.

Turkish PII Detection & Masking Benchmark (Saturday Labs, Belgin & BTX24 Datasets)

Span recall on three public Turkish PII datasets (a value counts as found when at least half of it is masked), measured with scripts/benchmark_pii_datasets.py from the local Hugging Face cache:

Dataset	Values	Recall	Over-masked characters	Notes
Saturday Labs (`cagrigungor/turkish-pii-masking-benchmark`, 1,000 rows)	2,096	94.5%	20%	Values aligned from the expected masked output; 100% on VKN, phone, blood type, passwords, IP and MAC addresses
Belgin (`negentropi/belgin-pii-dataset`, all splits)	1,851	74.0%	2.1%	Private dates 292/292, addresses 99.4%; 0/300 public dates (holidays, campaigns) masked
BTX24 (`BTX24/turkish-privacy-pii-ner`, test)	10,393	89.6%	1.2%	IBAN 99.6%, phone 99.9%, email 97.4%, person 92.3%
Throughput		`~4,600 docs/sec`		Short messages (about 90 characters), one CPU core, no neural network

The previous version of this table (94.1% on 200 Saturday Labs samples) had no script behind it; on the same three datasets the 0.6.0 engine reaches 85.6%, 50.9% and 58.4%.

🛡️ KVKK Compliance: Algorithmic validation for Turkish IDs (TCKN, VKN, IBAN, Driving License, Plate) prevents false positives while capturing authentic identifiers.
🔄 100% Reversible Restoration: Restores anonymized text losslessly with Turkish morphological vowel harmony adjustment on inflected suffixes.

Python Quickstart

Installation

# Using uv
uv pip install akana

# Using pip
pip install akana

Usage in Python

import akana

# 1. Advanced Grammatical Error Correction (GEC) & Diagnostics
text = "Ali de geldi, Veli te geldi. Evi terketmek zorunda kaldı ve 1923'de kurulan cumhuriyeti andık."

# Direct correction
corrected = akana.correct_grammar(text)
print("Corrected:", corrected)
# -> "Ali de geldi, Veli de geldi. Evi terk etmek zorunda kaldı ve 1923'te kurulan cumhuriyeti andık."

# Detailed diagnostic findings (offsets are characters; confidence is the rule's measured precision)
res = akana.check_grammar(text)
for f in res.findings:
    print(f"[{f.rule}] '{f.original_text}' -> '{f.replacement}' ({f.confidence:.2f}) | {f.message_tr}")

# Rule selection: the default rules are the ones measured precise on correct text; the
# experimental ones run only when asked for. Every call and GrammarChecker take the same arguments.
for r in akana.grammar_rules():
    print(r["id"], r["default"], r["confidence"], r["summary"])
akana.correct_grammar("Pazardan üç elmalar aldım.")                      # unchanged: quantity-plural is experimental
akana.correct_grammar("Pazardan üç elmalar aldım.", experimental=True)   # -> "Pazardan üç elma aldım."
akana.check_grammar(text, disable=["capitalization"], enable=["quantity-plural"])
checker = akana.GrammarChecker(rules=["de-da-hardened", "number-suffix"])
checker.enable_rule("mi-merged")

# 2. Standard Morphological Analysis (Zemberek-Compatible Format)
morph = akana.Morphology()
parses = morph.analyze("kitabıma")
for parse in parses:
    print(parse["lemma"], parse["primary_pos"], parse["morphemes"])
    print(parse["analysis"])
# -> kitap Noun ['Noun', 'P1sg', 'Dat']
# -> [kitap:Noun] kitab:Noun+A3sg+ım:P1sg+a:Dat    (Zemberek's format: every morpheme and its surface)

# `lemma` is the folded, lower-case key that sparse terms use; `display_lemma` spells it as
# the dictionary does, and `analysis` shows that spelling, as Zemberek prints it. `pos` is
# the POS after derivation: a participle reads as a noun and as an adjective with the same
# akana tags, and `pos` and `analysis` tell them apart.
p = morph.analyze("Ankara'dan")[0]
print(p["lemma"], p["display_lemma"], p["analysis"])
# -> ankara Ankara [Ankara:Noun,Prop] ankara:Noun+A3sg+dan:Abl
print([(p["pos"], p["analysis"]) for p in morph.analyze("olduğu")][:2])
# -> [('Noun', '[olmak:Verb] ol:Verb|duğ:PastPart→Noun+A3sg+u:P3sg'),
#     ('Adj', '[olmak:Verb] ol:Verb|duğ:PastPart→Adj+u:P3sg')]

# 3. Google-Style Syntactic Expressive Morphology (Inflectional Groups & UD)
syn_parses = akana.syntactic_analyze("geldiğimizde")
for p in syn_parses:
    print(p.formatted)
    # Output: (gel[VB]+[Polarity=Pos])([NOMP]-PastNom+[Case=Loc]+[Number=Sing]+[Number[psor]=Plur]+[Person=3]+[Person[psor]=1]+[VerbForm=Vnoun])+[Proper=False]
    for ig in p.inflectional_groups:
        print(f"  • IG [{ig.pos}] Deriv: {ig.derivation} -> {ig.features}")

# 4. Morphological Generation
surface = morph.generate("kitap", ["Noun", "A3sg", "P1sg", "Dat"])
print(surface)  # -> kitabıma

# 5. Spell Checking with StringZilla SIMD
spell = akana.SpellChecker()
print("Is 'kitap' correct?", spell.is_correct("kitap"))
suggestions = spell.suggest("ktap", max_distance=2, max_suggestions=3)
print("Suggestions for 'ktap':", [s["word"] for s in suggestions])

# 6. De-asciification & Normalization
print(akana.deasciify("turkce nlp cok hizli calisiyor"))
# -> türkçe nlp çok hızlı çalışıyor

print(akana.normalize_informal("nooldu ya yapcam dedim"))
# -> ne oldu ya yapacağım dedim

# 7. Compound Word Decomposition
compounds = akana.decompose_compound("denizaltı")
print(compounds)
# -> [{'surface': 'denizaltı', 'part1': 'deniz', 'part2': 'altı', ...}]

# 8. Modern Turkish Readability Analysis (Kalyoncu 2025 & Classic)
report = akana.analyze_readability("Küçük çocuk bahçede neşeyle koşuyordu.")
print(f"Kalyoncu F1: {report.kalyoncu_formula1.score} ({report.kalyoncu_formula1.grade_level})")
print(f"Ateşman: {report.atesman.score} ({report.atesman.grade_level})")

# 9. Turkish AI Writing Style Auditor & Actionable Humanizer Prompt
audit = akana.audit_ai_style("Yapay zeka teknolojileri, modern dünyada kritik bir rol oynamaktadır. Bu bağlamda —özellikle veri alanında— hayati önem taşımaktadır.")
print(f"Style score: {audit.ai_score}/100 ({audit.verdict})")  # rule-based style findings

prompt = akana.humanize_prompt("Bu doğrultuda hayati önem taşımaktadır.", register="blog")
print(prompt)

# Trained AI-text detector inside the audit report (a probability, not proof; needs 65+ words)
story = (  # Turkish Wikisource, public domain
    "Nasreddin Hoca'nın bir oğlu varmış. Ne denirse tam tersini yaparmış. Bir gün Nasreddin Hoca "
    "ile oğlu Akşehir'e gidip oradan iki çuval tuz almışlar. Eşeğe yükleyip köylerine dönmek için "
    "yola koyulmuşlar. Köylerinin yakınındaki dereye geldiklerinde, Nasreddin Hoca, derenin en sığ "
    "yerinden karşı tarafa geçmiş. Oğlu da eşeği sudan geçirmeye çalışıyormuş. Ama eşeğin "
    "sırtındaki çuvallardan biri, neredeyse suya değmek üzereymiş. Hoca, oğlunun ters huyunu "
    "bildiği için seslenmiş: Sevgili oğlum. Çuvallardan biri suya değecek. Biraz daha asıl da, "
    "iyice suya gömülsün. Ne var ki çocuğun o gün uysallığı üzerindeymiş. Tutup, babasının "
    "dediğini yapmış. Neticede iki çuval birden suyun içine düşmüş."
)
detection = akana.audit_ai_style(story).ai_detection
print(detection["verdict"], detection["ai_probability"])  # -> human 0.006… (ai / uncertain / human / too_short / out_of_scope)
print(detection["report"])  # Turkish "why AI" report

# 10. High-Level Turkish NLP Suite
# Syllabification & Hyphenation
print(akana.syllabify("Türkçe"))     # -> ['Türk', 'çe']
print(akana.hyphenate("bilgisayar")) # -> 'bil-gi-sa-yar'

# Number to Words Converter
print(akana.number_to_words(1923))            # -> 'bin dokuz yüz yirmi üç'
print(akana.currency_to_words(1250.50, "TL"))  # -> 'bin iki yüz elli lira elli kuruş'

# Named Entity Recognition (NER)
entities = akana.extract_entities("Prof. Dr. Ahmet Yılmaz 16 Ağustos 2026 tarihinde 500 TL ödeme yaptı.")
for e in entities:
    print(f"[{e.label}] {e.text}")

# Keyword Extraction (Turkish RAKE) & Extractive Summarization (TextRank)
keywords = akana.extract_keywords("Doğal dil işleme ve morfolojik analiz...", top_k=5)
summary = akana.summarize("Uzun metin...", max_sentences=2)

# 11. TurboQuant 2-Bit Turkish Sentence Embeddings & Semantic Similarity
vec = akana.embed("Türkiye'nin başkenti Ankara'dır.")
print(f"Vector dim: {len(vec)}")  # -> 256

# Semantic cosine similarity
score = akana.similarity("ev", "evler")
print(f"Similarity: {score:.4f}")  # -> ~0.9130

# Batch embedding
vecs = akana.embed_batch(["Merhaba dünya", "Hava bugün çok güzel"])
print(f"Batch size: {len(vecs)}")  # -> 2

# 12. Ultra-Low-Memory Packed Embeddings & USearch Integration (1M Docs in 64 MB RAM)
from usearch.index import Index, CompiledMetric, MetricKind, MetricSignature
import numpy as np

# A. 2-Bit TurboQuant Packed Vectors (64 bytes per document)
vec_2bit = akana.embed_packed_2bit("Türkçe doğal dil işleme kütüphanesi")
print(f"Packed 2-bit bytes: {len(vec_2bit)}")  # -> 64 bytes!

# Create zero-copy USearch index with Akana's pre-compiled C-ABI metric
metric_2bit = CompiledMetric(
    pointer=akana.get_usearch_metric_pointer_2bit(),
    kind=MetricKind.Cos,
    signature=MetricSignature.ArrayArraySize,
)
# USearch reads uint8 arrays as packed bits: a b1 index of 8 * 64 dims stores the 64 bytes as given
# (a u8 index of 64 dims would keep only their first 64 bits)
index = Index(ndim=64 * 8, metric=metric_2bit, dtype="b1")
index.add(0, np.frombuffer(vec_2bit, dtype=np.uint8))

# B. 1-Bit Binary Packed Vectors (32 bytes per document - 32 MB for 1M docs)
vec_1bit = akana.embed_packed_1bit("Hızlı vektör araması")
metric_1bit = CompiledMetric(
    pointer=akana.get_usearch_metric_pointer_1bit(),
    kind=MetricKind.Hamming,
    signature=MetricSignature.ArrayArraySize,
)
index_1bit = Index(ndim=32 * 8, metric=metric_1bit, dtype="b1")
index_1bit.add(0, np.frombuffer(vec_1bit, dtype=np.uint8))

# C. Morphological BM25 Sparse Embeddings (Agglutinative roots, lemmas & compounds)
sparse = akana.sparse_embed("Kitaplarımı çalışma masasına bıraktım.")
print(sparse["terms"])    # -> ['kitap', 'masa', 'çalışmak', 'bırakmak']: one lemma per word, in context
print(sparse["indices"])  # -> 32-bit term ids (ascending) for Qdrant / Milvus / Inverted Index
print(sparse["values"])   # -> BM25-saturated occurrence counts x POS weight x function-lemma IDF

# D. Reciprocal Rank Fusion (RRF) for Hybrid Retrieval
fused_results = akana.reciprocal_rank_fusion(
    dense_results=[(101, 0.95), (102, 0.88)],
    sparse_results=[(102, 14.2), (103, 11.0)],
    k=60.0
)

# 13. Turkish Text Chunking for RAG & LLMs (Chonkie-Inspired)
# A. SemanticChunker: Splits at natural topic shifts using TurboQuant embeddings
semantic_chunker = akana.SemanticChunker(
    chunk_size=512,
    threshold_mode="percentile",  # "percentile", "similarity", "stdev", "iqr", "auto"
    threshold_value=0.75,
    min_chunk_size=20,
    min_sentences_per_chunk=1,
    use_smoothing=True,
)
chunks = semantic_chunker(
    "Kuantum mekaniği atom altı parçacıkları inceler... "
    "Fenerbahçe dün akşam derbide galip geldi... "
    "Geleneksel Türk mutfağında baklava çok meşhurdur..."
)
for chunk in chunks:
    print(f"Chunk ({chunk.token_count} tokens, chars {chunk.start_index}:{chunk.end_index}): {chunk.text}")
    print("Sentences:", chunk.sentences)

# B. SentenceChunker: Fast sentence packing with token constraints and overlaps
sentence_chunker = akana.SentenceChunker(chunk_size=256, chunk_overlap=30)
sentence_chunks = sentence_chunker("Metin 1... Metin 2...")

# C. SDPMChunker: Semantic Double-Pass Merge for dense, cohesive chunks
sdpm_chunker = akana.SDPMChunker(
    chunk_size=512,
    threshold_mode="percentile",
    threshold_value=0.75,
    merge_threshold=0.65,
)
sdpm_chunks = sdpm_chunker("Uzun doküman...")

# D. Dict / JSON serialization
chunk_dict = chunks[0].to_dict()
print(chunk_dict["text"], chunk_dict["start_index"], chunk_dict["end_index"])

# 13. Turkish PII Masking & Bi-Directional Restoration (KVKK-Aligned)
raw_text = (
    "Müşterimiz Ahmet Yılmaz, 10000000146 nolu TCKN ve TR33 0006 1005 1978 6457 8413 26 "
    "nolu IBAN ile 12.05.1988 doğumludur. Şifresi: Limon004_."
)

# A. Reversible Structured Placeholder Masking
masked = akana.pii_mask(raw_text, mode="placeholder")
print("Masked:", masked["masked_text"])
# -> "Müşterimiz {{AD_1}}, {{TCKN_1}} nolu TCKN ve {{IBAN_1}} nolu IBAN ile {{DOGUM_TARIHI_1}} doğumludur. Şifresi: {{SIFRE_1}}."
print("Mapping:", masked["mapping"])

# B. Lossless Restoration with Turkish Vowel Harmony
restored = akana.pii_restore(masked["masked_text"], masked["mapping"])
assert restored == raw_text

# Downstream inflected text restoration (vowel harmony preserved automatically):
inflected_text = "Ödeme {{AD_1}}'a yapılmış ve {{AD_1}}'ın dosyası onaylanmıştır."
print(akana.pii_restore(inflected_text, masked["mapping"]))
# -> "Ödeme Ahmet Yılmaz'a yapılmış ve Ahmet Yılmaz'ın dosyası onaylanmıştır."

# C. Realistic Turkish Synthetic Surrogates Mode
surrogate = akana.pii_mask(raw_text, mode="surrogate")
print("Surrogate:", surrogate["masked_text"])
# -> "Müşterimiz Can Demir, 52381940562 nolu TCKN ve TR92 0006 1000 ... doğumludur..."

# D. Keep the names of well-known public figures, mask everyone else
engine = akana.TurkishPiiEngine(preserve_public_figures=True)
print(engine.mask("Nazım Hikmet'i en çok Ahmet Yılmaz okurdu.")["masked_text"])
# -> "Nazım Hikmet'i en çok {{AD_1}} okurdu."

Offsets, threads and errors

Offsets are character offsets. text[start:end] is the span for named entities (start/end), grammar findings (start_offset/end_offset), style findings (start/end), PII entities and chunks (start_index/end_index).
Calls release the GIL, so a thread pool (a FastAPI server, a ThreadPoolExecutor ingestion job) runs them in parallel. embed_batch, embed_batch_hybrid and the packed batch functions also split a batch of 512 or more texts across the CPU cores themselves.
Objects can be configured while threads use them. Morphology.load_dictionary_*, SpellChecker.add_word, GrammarChecker.enable_rule/disable_rule and TurkishPiiEngine.enable_type/disable_type wait for the calls in progress on that object, and the calls after them see the change. A PiiVault that threads share serves one mask at a time.
Engines are built once per process. The lexicon and the embedding weights are loaded on first use and shared by every object, so the module-level functions (analyze, stem, audit_ai_style, chunk_*, pii_mask, ...) and constructors are cheap to call repeatedly.
An internal error raises RuntimeError (not PyO3's PanicException, which except Exception does not catch).

CLI Usage

The akana CLI supports direct text arguments or reading from file via -f, --file:

# Turkish PII Masking & Restoration (KVKK-Aligned)
akana pii-mask "Ahmet Yılmaz 10000000146 nolu TCKN ile başvurdu." --mode placeholder
akana pii-mask "Ali Kaya Kadıköy şubesine geldi." --mode surrogate
akana pii-mask -f confidential.txt --mode placeholder --json
akana pii-restore "Ödeme {{AD_1}}'a yapıldı." --mapping mapping.json

# Grammar checking (default rules; --experimental, --rules, --enable, --disable to choose)
akana grammar "Ali de geldi, Veli te geldi."
akana grammar --correct -f article.txt
akana grammar --experimental --disable capitalization --json "Pazardan üç elmalar aldım."
akana grammar --list-rules

# AI style auditing
akana ai-audit "Bu bağlamda kritik bir rol oynamaktadır."
akana ai-audit -f article.txt

# Generate humanizer rewrite prompt
akana humanize-prompt "Bu doğrultuda hayati önem taşımaktadır." --register blog

# Syntactic morphological analysis (Google FSMNLP format)
akana syntactic-analyze "geldiğimizde"

# Standard morphological analysis
akana analyze "evlerimizde"

# Readability analysis
akana readability "Küçük çocuk bahçede neşeyle koşuyordu."

# Syllabification & Number conversion
akana syllabify "bilgisayar"
akana number 1923

# De-asciification & Normalization
akana deasciify "turkce nlp"
akana normalize "yapcam"

# Universal Dependencies Parsing
akana parse "Ali güzel kitabı okudu."

# Turkish Sentence Embeddings & Similarity
akana embed "Türkiye'nin başkenti Ankara'dır."
akana similarity "ev" "evler"

# Text Chunking (Semantic, Sentence, SDPM)
akana chunk --strategy semantic --chunk-size 512 --mode percentile -t 0.75 -f document.txt
akana chunk --strategy sentence --chunk-size 256 --overlap 30 -f document.txt
akana chunk --strategy sdpm --chunk-size 512 --json -f document.txt

Rust Crate Usage (`akana-core`)

Add to Cargo.toml:

[dependencies]
akana-core = { version = "0.5", default-features = true }

use akana_core::chunking::{SemanticChunker, SentenceChunker, SDPMChunker, ThresholdMode};
use akana_core::grammar::{GrammarRule, RuleSet, TurkishGrammarChecker};
use akana_core::morphology::TurkishMorphology;
use akana_core::syntactic_morphology::TurkishSyntacticMorphology;
use akana_core::embeddings::TurkishEmbeddings;
use akana_core::pii::{TurkishPiiEngine, PiiMode};
use akana_core::phonology::to_turkish_lower;

fn main() {
    let lower = to_turkish_lower("İSTANBUL");
    println!("Lower: {}", lower);

    // 1. Semantic Chunking with TurboQuant Embeddings
    let chunker = SemanticChunker::new(512, ThresholdMode::Percentile(0.75));
    let chunks = chunker.chunk("Kuantum mekaniği... Fenerbahçe dün akşam...");
    for chunk in chunks {
        println!("Chunk ({} tokens, {}-{}): {}", chunk.token_count, chunk.start_index, chunk.end_index, chunk.text);
    }

    // 2. Turkish Sentence Embeddings (TurboQuant 2-Bit)
    let embeddings = TurkishEmbeddings::new();
    let vec = embeddings.embed("Türkiye'nin başkenti Ankara'dır.");
    println!("Embedding dim: {}", vec.len()); // 256
    let sim = embeddings.similarity("ev", "evler");
    println!("Similarity: {:.4}", sim); // 0.9130

    // 3. Grammatical Error Correction & Diagnostics (the default rules)
    let grammar_checker = TurkishGrammarChecker::new();
    let res = grammar_checker.check("Ali de geldi, Veli te geldi. Cumhuriyet 1923'de kuruldu.");
    println!("Corrected: {}", res.corrected);
    for f in &res.findings {
        println!("[{}] '{}' -> '{}' ({:.2})", f.rule, f.original_text, f.replacement, f.confidence);
    }
    // experimental rules on request, by id or all of them
    let all = RuleSet::default().with_experimental();
    println!("{}", grammar_checker.correct_with_rules("Pazardan üç elmalar aldık.", all));
    let mut only_clitics = TurkishGrammarChecker::with_rules(RuleSet::empty());
    only_clitics.enable_rule(GrammarRule::DeDaHardened);

    // 4. Standard Morphology
    let morph = TurkishMorphology::new();
    let parses = morph.analyze("kitabım");
    for p in parses {
        println!("{}", p.formatted);
    }

    // 5. Syntactic Expressive Morphology (Inflectional Groups)
    let syn_morph = TurkishSyntacticMorphology::new();
    let syn_parses = syn_morph.analyze("geldiğimizde");
    for p in syn_parses {
        println!("{}", p.formatted);
    }

    // 6. Turkish PII Masking & Bi-Directional Restoration (KVKK-Aligned)
    let pii_engine = TurkishPiiEngine::new();
    let pii_res = pii_engine.mask(
        "Ahmet Yılmaz 10000000146 nolu TCKN ile TR33 0006 1005 1978 6457 8413 26 nolu hesaptan işlem yaptı.",
        PiiMode::Placeholder,
    );
    println!("Masked: {}", pii_res.masked_text);

    // Lossless restoration with automatic vowel harmony adjustment
    let restored = pii_engine.restore_with_mapping(
        &format!("İşlem {}'a ait hesaba başarıyla yansıtıldı.", "{{AD_1}}"),
        &pii_res.mapping,
    );
    println!("Restored: {}", restored);
}

Developer Guide & Publishing

For local development setup, testing, running benchmarks, compiling native wheels, and publishing releases to PyPI & crates.io, see the Developer & Maintainer Guide.

Acknowledgements & Academic Citations

Akana builds upon decades of pioneering linguistic and natural language processing research in Turkish. We gratefully acknowledge and credit:

Kemal Oflazer: Foundational two-level Turkish morphological analysis (1994) and the Inflectional Group (IG) representation (2003) for Turkish dependency syntax.
Ahmet A. Akın & The Zemberek Team: Open-source Turkish morphology, phonotactics, and extensive root vocabulary database.
Oğuzhan Güngör & Zeyrek Contributors: The pure-Python Zemberek port that inspired modern open Turkish NLP tooling.
Koç University GGLab (Duygu Ataman & Co-authors): "GECTurk: Grammatical Error Correction and Detection Dataset for Turkish" (arXiv:2309.11346), providing the 25-category Turkish grammatical error taxonomy and benchmark datasets.
Google Research (Adnan Öztürel, Tolga Kayadelen, Işın Demirşahin): "A Syntactically Expressive Morphological Analyzer for Turkish" (FSMNLP 2019), introducing zero-derivation elimination and two-level inflectional group FSTs.
Mustafa Kalyoncu & Co-authors (2025): Development of modern Turkish readability formulas (Formulas 1–4) and the empirical 4,600-word grade-level familiarity corpus.
Ender Ateşman (1997), Çetinkaya-Uzun (2010), Bezirci-Yılmaz (2010): Classical readability research for the Turkish education system.
Ash Vardanian & Unum Cloud: StringZilla, providing hardware-accelerated SIMD vector search and edit distance algorithms.
Bushra Beg (Turkce-Humanizer): Research into Turkish AI writing style signatures, calques, and stylistic heuristics.

License

Licensed under either of:

at your option.

akana-cli 0.9.0